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Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
| Version: | 1.0.1 |
| Depends: | R (≥ 2.10) |
| Imports: | randomForest, foreach, dplyr, parallel, doParallel |
| Published: | 2018-05-01 |
| DOI: | 10.32614/CRAN.package.Sstack |
| Author: | Kevin Matlock, Raziur Rahman |
| Maintainer: | Kevin Matlock <kevin.matlock at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | Sstack results |
| Reference manual: | Sstack.pdf |
| Package source: | Sstack_1.0.1.tar.gz |
| Windows binaries: | r-devel: Sstack_1.0.1.zip, r-release: Sstack_1.0.1.zip, r-oldrel: Sstack_1.0.1.zip |
| macOS binaries: | r-release (arm64): Sstack_1.0.1.tgz, r-oldrel (arm64): Sstack_1.0.1.tgz, r-release (x86_64): Sstack_1.0.1.tgz, r-oldrel (x86_64): Sstack_1.0.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.